SoroushVahidi/scidocs huggingface dataset:
Deep convolutional neural network for latent fingerprint enhancement
(2018)

In this work, we propose a novel latent fingerprint enhancement method based on FingerNet inspired by recent development of Convolutional Neural Network (CNN). Although CNN is achieving superior performance in many computer vision tasks from low-level image processing to high-level semantic understanding, limited attention has been paid in fingerprint community. The proposed FingerNet has three major parts: one common convolution part shared by two different deconvolution parts, which are the enhancement branch and the orientation branch. The convolution part is to extract fingerprint features particularly for enhancement purpose. The enhancement deconvolution branch is employed to remove structured noise and enhance fingerprints as its task. The orientation deconvolution branch performs the task of guiding enhancement through a multi-task learning strategy. The network is trained in the manner of pixels-to-pixels and end-to-end learning, that can directly enhance latent fingerprint as the output. We also study some implementation details such as single-task learning, multi-task learning, and the residual learning. Experimental results of the FingerNet system on latent fingerprint dataset NIST SD27 demonstrate effectiveness and robustness of the proposed method.
IRIS performer: a high performance multiprocessing toolkit for real-time 3D graphics
(1994)

This paper describes the design and implementation of IRIS Performer, a toolkit for visual simulation, virtual reality, and other real-time 3D graphics applications. The principal design goal is to allow application developers to more easily obtain maximal performance from 3D graphics workstations which feature multiple CPUs and support an immediate-mode rendering library. To this end, the toolkit combines a low-level library for high-performance rendering with a high-level library that implements pipelined, parallel traversals of a hierarchical scene graph. While discussing the toolkit architecture, the paper illuminates and addresses performance issues fundamental to immediate-mode graphics and coarse-grained, pipelined multiprocessing. Graphics optimizations focus on efficient data transfer to the graphics subsystem, reduction of mode settings, and restricting state inheritance. The toolkit's multiprocessing features solve the problems of how to partition work among multiple processes, how to synchronize these processes, and how to manage data in a pipelined, multiprocessing environment. The paper also discusses support for intersection detection, fixed-frame rates, run-time profiling and special effects such as geometric morphing.
Tversky loss function for image segmentation using 3D fully convolutional deep networks
(2017)

Fully convolutional deep neural networks carry out excellent potential for fast and accurate image segmentation. One of the main challenges in training these networks is data imbalance, which is particularly problematic in medical imaging applications such as lesion segmentation where the number of lesion voxels is often much lower than the number of non-lesion voxels. Training with unbalanced data can lead to predictions that are severely biased towards high precision but low recall (sensitivity), which is undesired especially in medical applications where false negatives are much less tolerable than false positives. Several methods have been proposed to deal with this problem including balanced sampling, two step training, sample re-weighting, and similarity loss functions. In this paper, we propose a generalized loss function based on the Tversky index to address the issue of data imbalance and achieve much better trade-off between precision and recall in training 3D fully convolutional deep neural networks. Experimental results in multiple sclerosis lesion segmentation on magnetic resonance images show improved F2 score, Dice coefficient, and the area under the precision-recall curve in test data. Based on these results we suggest Tversky loss function as a generalized framework to effectively train deep neural networks.
Implementation of FC-TCR for Reactive Power Control
(2013)

This paper deals with the simulation and implementation of fixed capacitor thyristor controlled reactor system(SVC) . The TCR system is simulated using MATLAB and the simulation results are presented. SVC is basically a shunt connected static var generator whose output is adjusted to exchange capacitive or inductive current so as to maintain or control specific power variable. In this paper, simple circuit model of Thyristor Controlled Reactor is modeled and simulated using MATLAB. The simulation results are presented. The current drawn by the TCR varies with the variation in the firing angle. The simulation results are compared with the theoretical results.
Deep Automatic Portrait Matting
(2016)

We propose an automatic image matting method for portrait images. This method does not need user interaction, which was however essential in most previous approaches. In order to accomplish this goal, a new end-to-end convolutional neural network (CNN) based framework is proposed taking the input of a portrait image. It outputs the matte result. Our method considers not only image semantic prediction but also pixel-level image matte optimization. A new portrait image dataset is constructed with our labeled matting ground truth. Our automatic method achieves comparable results with state-of-the-art methods that require specified foreground and background regions or pixels. Many applications are enabled given the automatic nature of our system.
Prediction of Primary Pupil Enrollment in Government School Using Data Mining Forecasting Technique
(2014)

This research concentrates upon predictive analysis of enrolled pupil using forecasting based on data mining technique. The Microsoft SQL Server Data Mining Add-ins Excel 2007 was employed as a software mining tool for predicting enrollment of pupils. The time series algorithm was used for experiment analysis. U-DISE (Unified District Information System for Education) Dataset of Aurangabad district in Maharashtra (India) was obtained from SSA (Sarva Shiksha Abhiyan) and was used for analysis. Data mining for primary pupil enrollment research provides a feasible way to analyze the trend and to addresses the interest of pupil. The dataset was studied and analyzed to forecast registered pupils in government for upcoming years. We conclude that for upcoming year, pupil strength in government school will be dropped down and number of teachers will be supersizing in the said district. Keywords— Data mining, Microsoft SQL Server Data Mining Add-ins Excel 2007, Time series algorithm, U-DISE
60-GHz Circularly Polarized U-Slot Patch Antenna Array on LTCC
(2013)

This communication presents a 60-GHz wideband circularly polarized (CP) U-slot patch antenna array of 4 × 4 elements on low temperature cofired ceramic (LTCC). A CP U-slot patch antenna is used as the array element to enhance the impedance bandwidth and a stripline sequential rotation feeding scheme is applied to achieve wide axial ratio (AR) bandwidth. Meanwhile, a grounded coplanar waveguide (GCPW) to stripline transition is designed for probe station measurement. The fabricated antenna array has a dimension of 14 ×16 × 1.1 mm3 . The simulated and measured impedance bandwidths, AR bandwidths, and radiation patterns are investigated and compared. Measured results show that the proposed antenna array has a wide impedance bandwidth from 50.5 GHz to 67 GHz for |S11| <; -10 dB, and a wide AR bandwidth from 54 GHz to 65.5 GHz for AR <; 3 dB. In addition, it exhibits a peak gain of 16 dBi and a beam-shaped pattern with 3-dB beam width of 20°. Moreover, its AR keeps below 3 dB within the 3-dB beam width.
Wideband Millimeter-Wave Substrate Integrated Waveguide Cavity-Backed Rectangular Patch Antenna
(2014)

In this letter, a new type of wideband substrate integrated waveguide (SIW) cavity-backed patch antenna and array for millimeter wave (mmW) are investigated and implemented. The proposed antenna is composed of a rectangular patch with a backed SIW cavity. In order to enhance the bandwidth and radiation efficiency, the cavity is designed to resonate at its TE210 mode. Based on the proposed antenna, a 4 × 4 array is also designed. Both the proposed antenna and array are fabricated with standard printed circuit board (PCB) process, which possess the advantage of easy integration with planar circuits. The measured bandwidth (|S11| ≤ -10 dB) of the antenna element is larger than 15%, and that of the antenna array is about 8.7%. The measured peak gains are 6.5 dBi for the element and 17.8 dBi for the array, and the corresponding simulated radiation efficiencies are 83.9% and 74.9%, respectively. The proposed antenna and array are promising for millimeter-wave applications due to its merits of wide band, high efficiency, low cost, low profile, etc.
Millimeter-Wave Microstrip Comb-Line Antenna Using Reflection-Canceling Slit Structure
(2011)

A microstrip comb-line antenna is developed in the millimeter-wave band. When the element spacing is one guide wavelength for the broadside beam in the traveling-wave excitation, reflections from all the radiating elements are synthesized in phase. Therefore, the return loss increases significantly. Furthermore, re-radiation from elements due to the reflection wave degrades the design accuracy for the required radiation pattern. We propose the way to improve the reflection characteristic of the antenna with arbitrary beam directions including strictly a broadside direction. To suppress the reflection, we propose a reflection-canceling slit structure installed on the feeding line around each radiating element. A 27-element linear array antenna with a broadside beam is developed at 76.5 GHz. To confirm the feasibility of the simple design procedure, the performance is evaluated through the measurement in the millimeter-wave band.
Low Cost Planar Waveguide Technology-Based Dielectric Resonator Antenna (DRA) for Millimeter-Wave Applications: Analysis, Design, and Fabrication
(2010)

A compact, low cost and high radiation efficiency antenna structure, planar waveguide, substrate integrated waveguide (SIW), dielectric resonator antennas (DRA) is presented in this paper. Since SIW is a high Q- waveguide and DRA is a low loss radiator, then SIW-DRA forms an excellent antenna system with high radiation efficiency at millimeter-waveband, where the conductor loss dominates. The impact of different antenna parameters on the antenna performance is studied. Experimental data for SIW-DRA, based on two different slot orientations, at millimeter-wave band are introduced and compared to the simulated HFSS results to validate our proposed antenna model. A good agreement is obtained. The measured gain for SIW-DRA single element showed a broadside gain of 5.51 dB,-19 dB maximum cross polarized radiation level, and overall calculated (simulated using HFSS) radiation efficiency of greater than 95%.
Context-Aware Bandits
(2015)

In this paper, we present a simple and efficient Context-Aware Bandit (CAB) algorithm. With CAB we attempt to craft a bandit algorithm that can capture collaborative effects and that can be easily deployed in a real-world recommendation system, where the multi-armed bandits have been shown to perform well in particular with respect to the cold-start problem. CAB utilizes a context-aware clustering technique augmenting exploration-exploitation strategies. CAB dynamically clusters the users based on the content universe under consideration. We provide a theoretical analysis in the standard stochastic multi-armed bandits setting. We demonstrate the efficiency of our approach on production and real-world datasets, showing the scalability and, more importantly, the significantly increased prediction performance against several existing state-of-theart methods.
Occupational accidents aboard merchant ships.
(2002)

OBJECTIVES
To investigate the frequency, circumstances, and causes of occupational accidents aboard merchant ships in international trade, and to identify risk factors for the occurrence of occupational accidents as well as dangerous working situations where possible preventive measures may be initiated.


METHODS
The study is a historical follow up on occupational accidents among crew aboard Danish merchant ships in the period 1993-7. Data were extracted from the Danish Maritime Authority and insurance data. Exact data on time at risk were available.


RESULTS
A total of 1993 accidents were identified during a total of 31 140 years at sea. Among these, 209 accidents resulted in permanent disability of 5% or more, and 27 were fatal. The mean risk of having an occupational accident was 6.4/100 years at sea and the risk of an accident causing a permanent disability of 5% or more was 0.67/100 years aboard. Relative risks for notified accidents and accidents causing permanent disability of 5% or more were calculated in a multivariate analysis including ship type, occupation, age, time on board, change of ship since last employment period, and nationality. Foreigners had a considerably lower recorded rate of accidents than Danish citizens. Age was a major risk factor for accidents causing permanent disability. Change of ship and the first period aboard a particular ship were identified as risk factors. Walking from one place to another aboard the ship caused serious accidents. The most serious accidents happened on deck.


CONCLUSIONS
It was possible to clearly identify work situations and specific risk factors for accidents aboard merchant ships. Most accidents happened while performing daily routine duties. Preventive measures should focus on workplace instructions for all important functions aboard and also on the prevention of accidents caused by walking around aboard the ship.
Dynamic intra- and interhemispheric interactions during unilateral and bilateral hand movements assessed with fMRI and DCM
(2008)

Any motor action results from a dynamic interplay of various brain regions involved in different aspects of movement preparation and execution. Establishing a reliable model of how these areas interact is crucial for a better understanding of the mechanisms underlying motor function in both healthy subjects and patients. We used fMRI and dynamic causal modeling to reveal the specific excitatory and inhibitory influences within the human motor system for the generation of voluntary hand movements. We found an intrinsic balance of excitatory and inhibitory couplings among core motor regions within and across hemispheres. Neural coupling within this network was specifically modulated upon uni- and bimanual movements. During unimanual movements, connectivity towards the contralateral primary motor cortex was enhanced while neural coupling towards ipsilateral motor areas was reduced by both transcallosal inhibition and top-down modulation. Bimanual hand movements were associated with a symmetric facilitation of neural activity mediated by both increased intrahemispheric connectivity and enhanced transcallosal coupling of SMA and M1. The data suggest that especially the supplementary motor area represents a key structure promoting or suppressing activity in the cortical motor network driving uni- and bilateral hand movements. Our data demonstrate that fMRI in combination with DCM allows insights into intrinsic properties of the human motor system and task-dependent modulations thereof.
Stereo HDR Disparity Map Computation Using Structured Light
(2014)

In this paper, we present work in progress towards the generation of a ground truth data set for High Dynamic Range (HDR) stereo matching. The development and evaluation of novel stereo matching algorithms that are tailored to the characteristics of HDR images would greatly benefit from the availability of such reference data. We describe our laboratory setup and processing steps for acquiring multi-exposed stereo images along with a corresponding reference disparity map computed by a structured light approach. We discuss the special requirements and challenges which HDR test scenes impose on the computation of the reference disparities and show some preliminary results.
Metamodel for Service Design and Service Innovation: Integrating Service Activities, Service Systems, and Value Constellations
(2011)

This paper presents a metamodel that addresses service system design and innovation by traversing and integrating three essential layers, service activities, service systems, and value constellations. The metamodel's approach to service systems as service-inoperation is an alternative to another currently used approach that views service systems as systems of economic exchange. The metamodel addresses service science topics including basic concepts of service science, design thinking for service systems, decomposition within service systems, and integration of IT service architecture with
Accessing Structured Health Information through English Queries and Automatic Deduction
(2011)

While much health data is available online, patients who are not technically astute may be unable to access it because they may not know the relevant resources, they may be reluctant to confront an unfamiliar interface, and they may not know how to compose an answer from information provided by multiple heterogeneous resources. We describe ongoing research in using natural English text queries and automated deduction to obtain answers based on multiple structured data sources in a specific subject domain. Each English query is transformed using natural language technology into an unambiguous logical form; this is submitted to a theorem prover that operates over an axiomatic theory of the subject domain. Symbols in the theory are linked to relations in external databases known to the system. An answer is obtained from the proof, along with an English language explanation of how the answer was obtained. Answers need not be present explicitly in any of the databases, but rather may be deduced or computed from the information they provide. Although English is highly ambiguous, the natural language technology is informed by subject domain knowledge, so that readings of the query that are syntactically plausible but semantically impossible are discarded. When a question is still ambiguous, the system can interrogate the patient to determine what meaning was intended. Additional queries can clarify earlier ones or ask questions referring to previously computed answers. We describe a prototype system, Quadri, which answers questions about HIV treatment using the Stanford HIV Drug Resistance Database and other resources. Natural language processing is provided by PARC’s Bridge, and the deductive mechanism is SRI’s SNARK theorem prover. We discuss some of the problems that must be faced to make this approach work, and some of our solutions.
Automatic categorization of medical images for content-based retrieval and data mining.
(2005)

Categorization of medical images means selecting the appropriate class for a given image out of a set of pre-defined categories. This is an important step for data mining and content-based image retrieval (CBIR). So far, published approaches are capable to distinguish up to 10 categories. In this paper, we evaluate automatic categorization into more than 80 categories describing the imaging modality and direction as well as the body part and biological system examined. Based on 6231 reference images from hospital routine, 85.5% correctness is obtained combining global texture features with scaled images. With a frequency of 97.7%, the correct class is within the best ten matches, which is sufficient for medical CBIR applications.
Capturing and analyzing low-level events from the code editor
(2011)

In this paper, we present FLUORITE, a publicly available event logging plug-in for Eclipse which captures all of the low-level events when using the Eclipse code editor. FLUORITE captures not only what types of events occurred in the code editor, but also more detailed information such as the inserted and deleted text and the specific parameters for each command. This enables the detection of many usage patterns that could otherwise not be recognized, such as "typo correction" that requires knowing that the entered text is immediately deleted and replaced. Moreover, the snapshots of each source code file that has been opened during the session can be completely reproduced using the collected information. We also provide analysis and visualization tools which report various statistics about usage patterns, and we provide the logs in an XML format so others can write their own analyzers. FLUORITE can be used for not only evaluating existing tools, but also for discovering issues that motivate new tools.
Zero-Day Attack Identification in Streaming Data Using Semantics and Spark
(2017)

Intrusion Detection Systems (IDS) have been in existence for many years now, but they fall short in efficiently detecting zero-day attacks. This paper presents an organic combination of Semantic Link Networks (SLN) and dynamic semantic graph generation for the on the fly discovery of zero-day attacks using the Spark Streaming platform for parallel detection. In addition, a minimum redundancy maximum relevance (MRMR) feature selection algorithm is deployed to determine the most discriminating features of the dataset. Compared to previous studies on zero-day attack identification, the described method yields better results due to the semantic learning and reasoning on top of the training data and due to the use of collaborative classification methods. We also verified the scalability of our method in a distributed environment.
Leveraging social grouping for trust building in foreign electronic commerce firms: An exploratory study
(2013)

Internet development has fueled e-commerce firms’ globalization efforts, but many have met with only limited success. This often stems from the foreign firms’ limited understanding of a focal country’s local culture and idiosyncrasies. Foreign firms are usually viewed as out-group entities, which lowers consumers’ trust in them. The extent of such a phenomenon varies. In locations where people are more skeptical of out-groups, a critical question is whether it is possible to transform such foreign out-group firms into in-groups, specifically with the support of popular social networking media. Based on Social Identity Theory and Trust Transference Process, five strategies leveraging social grouping and social ties to build trust for foreign electronic commerce firms were proposed. A survey was conducted to examine their effectiveness. The results suggest that social-grouping strategies are useful for in-grouping foreign out-group entities to build trust, and the effectiveness of strategies is determined by the social similarity and psychological distance between the consumer and the endorser. This has important implications for scholars and practitioners, both local and abroad, to leverage social grouping to boost Internet sales. © 2013 Elsevier Ltd. All rights reserved.